llama3 README

The project's README from github.com/meta-llama/llama3. As one markdown file: README.md.

🤗 Models on Hugging Face  | Blog  | Website  | Get Started 

Note of deprecation

Thank you for developing with Llama models. As part of the Llama 3.1 release, we’ve consolidated GitHub repos and added some additional repos as we’ve expanded Llama’s functionality into being an e2e Llama Stack. Please use the following repos going forward:

If you have any questions, please feel free to file an issue on any of the above repos and we will do our best to respond in a timely manner.

Thank you!

(Deprecated) Meta Llama 3

We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers, and businesses of all sizes so that they can experiment, innovate, and scale their ideas responsibly.

This release includes model weights and starting code for pre-trained and instruction-tuned Llama 3 language models — including sizes of 8B to 70B parameters.

This repository is a minimal example of loading Llama 3 models and running inference. For more detailed examples, see llama-cookbook.

Download

To download the model weights and tokenizer, please visit the Meta Llama website and accept our License.

Once your request is approved, you will receive a signed URL over email. Then, run the download.sh script, passing the URL provided when prompted to start the download.

Pre-requisites: Ensure you have wget and md5sum installed. Then run the script: ./download.sh.

Remember that the links expire after 24 hours and a certain amount of downloads. You can always re-request a link if you start seeing errors such as 403: Forbidden.

Access to Hugging Face

We also provide downloads on Hugging Face, in both transformers and native llama3 formats. To download the weights from Hugging Face, please follow these steps:

huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir meta-llama/Meta-Llama-3-8B-Instruct

Quick Start

You can follow the steps below to get up and running with Llama 3 models quickly. These steps will let you run quick inference locally. For more examples, see the Llama Cookbook repository.

  1. Clone and download this repository in a conda env with PyTorch / CUDA.

  2. In the top-level directory run:

    pip install -e .

  3. Visit the Meta Llama website and register to download the model/s.

  4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the download.sh script.

  5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script.

    • Make sure to grant execution permissions to the download.sh script
    • During this process, you will be prompted to enter the URL from the email.
    • Do not use the “Copy Link” option; copy the link from the email manually.

  6. Once the model/s you want have been downloaded, you can run the model locally using the command below:

torchrun --nproc_per_node 1 example_chat_completion.py \
    --ckpt_dir Meta-Llama-3-8B-Instruct/ \
    --tokenizer_path Meta-Llama-3-8B-Instruct/tokenizer.model \
    --max_seq_len 512 --max_batch_size 6

Note

Inference

Different models require different model-parallel (MP) values:

Model MP
8B 1
70B 8

All models support sequence length up to 8192 tokens, but we pre-allocate the cache according to max_seq_len and max_batch_size values. So set those according to your hardware.

Pretrained Models

These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.

See example_text_completion.py for some examples. To illustrate, see the command below to run it with the llama-3-8b model (nproc_per_node needs to be set to the MP value):

torchrun --nproc_per_node 1 example_text_completion.py \
    --ckpt_dir Meta-Llama-3-8B/ \
    --tokenizer_path Meta-Llama-3-8B/tokenizer.model \
    --max_seq_len 128 --max_batch_size 4

Instruction-tuned Models

The fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, specific formatting defined in ChatFormat needs to be followed: The prompt begins with a <|begin_of_text|> special token, after which one or more messages follow. Each message starts with the <|start_header_id|> tag, the role system, user or assistant, and the <|end_header_id|> tag. After a double newline \n\n, the message's contents follow. The end of each message is marked by the <|eot_id|> token.

You can also deploy additional classifiers to filter out inputs and outputs that are deemed unsafe. See the llama-cookbook repo for an example of how to add a safety checker to the inputs and outputs of your inference code.

Examples using llama-3-8b-chat:

torchrun --nproc_per_node 1 example_chat_completion.py \
    --ckpt_dir Meta-Llama-3-8B-Instruct/ \
    --tokenizer_path Meta-Llama-3-8B-Instruct/tokenizer.model \
    --max_seq_len 512 --max_batch_size 6

Llama 3 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. To help developers address these risks, we have created the Responsible Use Guide.

Issues

Please report any software “bug” or other problems with the models through one of the following means:

Model Card

See MODEL_CARD.md.

License

Our model and weights are licensed for researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals and industry through this opportunity while fostering an environment of discovery and ethical AI advancements.

See the LICENSE file, as well as our accompanying Acceptable Use Policy

Questions

For common questions, the FAQ can be found here, which will be updated over time as new questions arise.

llama3 (meta-llama/llama3)

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Что это такое?Официальный репозиторий Meta на GitHub для Llama 3: веса моделей и минимальный код для запуска инференса на предобученных и дообученных под инструкции моделях размеров 8B и 70B. Репозиторий уже устарел (deprecated).
Чем отличаются pretrained-модели от instruction-tuned?Pretrained-чекпоинты не дообучены под чат — они продолжают ваш промпт (пример_text_completion.py). Instruction-tuned-версии обучены под диалог и должны использовать специальную структуру промпта ChatFormat (example_chat_completion.py).
Как мне скачать модель и запустить её локально?Склонируйте репозиторий в conda-окружение с PyTorch и CUDA, примите лицензию на сайте Meta Llama, затем запустите ./download.sh (нужны wget и md5sum) и вставьте подписанную ссылку, которую вам пришлют на почту. Примеры скриптов после этого запускают инференс на вашем чекпоинте.
Какое железо мне нужно для этих моделей?Conda-окружение с PyTorch и CUDA. Модель 8B работает на одной GPU (model parallelism 1), модели 70B нужно 8 GPU (MP=8), длина контекста — до 8192 токенов.
Это всё ещё тот репозиторий, который нужен для Llama?Нет — он заархивирован. С выходом Llama 3.1 Meta собрала всё в единый e2e Llama Stack: llama-models, PurpleLlama, llama-toolchain, llama-agentic-system и llama-cookbook.
Можно ли использовать Llama 3 в коммерческих целях?Да — лицензия распространяется и на исследователей, и на коммерческие компании, но это собственная community-лицензия Meta, а не стандартная open-source, и к ней приложена Acceptable Use Policy.
А можно скачать веса с Hugging Face?Да. Meta публикует все модели Llama 3 на Hugging Face и в нативном формате, и в формате transformers. Всё равно придётся принять лицензию и дождаться одобрения — раньше заявки рассматривались до часа.
Ссылка на скачивание перестала работать — что делать?Подписанные ссылки истекают через 24 часа, а также после определённого числа скачиваний. Если начали появляться ошибки вроде 403: Forbidden, просто запросите ссылку заново.
Там есть какие-нибудь инструменты для безопасности?В этом репозитории — нет. Meta отсылает к своему Responsible Use Guide и показывает в llama-cookbook, как добавить классификаторы, отфильтровывающие небезопасные входы и выходы; стек безопасности (PurpleLlama) живёт в новых репозиториях.
Обязательно ли использовать собственный код инференса Meta?Нет. Если взять версию для Hugging Face transformers, стандартный transformers pipeline сам скачает и закэширует веса. Python-код этого репозитория — лишь минимальный пример поверх нативных чекпоинтов.
What are similar projects?ai-torture-chamber, codellama, petals, Qwen, Chinese-LLaMA-Alpaca, airllm, llama, open-r1
Which topics is it in?instruction-tuning, meta, ai-safety, open-weights, llama, transformers, large-language-models, inference, deprecated, huggingface, cuda, pytorch
Creator
meta-llama
Size
0.6 MB
Stars
29.2K
Updated
CommitJan 26
Age
2y
Commits · 90 Days